|
|
|
| Does Dishonesty Risk Contagion Affect Bond Pricing？ An Empirical Study Based on Guarantee Network Big Data |
| WANG Lei, LI Xiaoteng, ZHANG Zili, ZHAO Xuejun
|
Guanghua School of Management, Peking University;
Harvest Fund Management |
|
|
|
Abstract The economic connections between enterprises result in tightly linked networks. Therefore, a corporation's dishonesty risk is not independent, but has a contagion effect in the surrounding corporate network. A credit guarantee network is an important medium for credit risk contagion. Extensive credit guarantee networks have been established among domestic enterprises. When an enterprise experiences dishonesty risk, the risk spreads along the guarantee network to the surrounding enterprises, which is a complex network dynamics process. However, traditional economic tools cannot effectively analyze this process; risk monitoring and prevention can be more effective through the application of big data and artificial intelligence.
Traditional bond credit research, whether it uses a structured or parsimonious model, holds that the credit spread of a corporation depends on its own business conditions, ignoring contagion effects among corporate networks. There have been few studies of the impact of dishonesty contagion on bond credit risk, and the following questions remain unanswered: Are bonds that have not incurred dishonesty risk affected by dishonesty risk contagion from other entities？ If a dishonesty risk contagion effect exists, does it have the same impact on enterprises with different ownership structures？
The guarantee network is a credit lending network among enterprises, and it also represents the contagion path of the dishonesty risk. Dishonesty risk, as a broad credit risk, has a contagion effect in the guarantee network, affecting the pricing of credit bonds. Three types of dishonesty risk contagion effects are identified in this research: a direct effect, a local effect, and a global diffusion effect.
Based on 433000 pieces of guarantee data from non-financial enterprises, this paper constructs a corporate credit guarantee network month by month, including 5,578 bond issuers. It also uses data of defaulters disclosed by the Supreme People’s Court and bond default data to identify the dishonest behaviors of enterprises.
The empirical results show that the three types of contagion effects have low correlations with each other, and thus they illustrate the effects from different perspectives. Contagion effects will cause the credit spreads of bond issuers to increase significantly. For direct and local contagion effects, the contagion effect of the guarantor has a greater impact on the bond credit spread than that of the guarantee, as the credit qualification of the guarantee is worse than that of the guarantor, and it is more susceptible to direct risk contagion. From the perspective of enterprise ownership, state-owned enterprise bonds are more sensitive to the global diffusion effect, and private enterprise bonds are more responsive to direct contagion effects and local contagion effects. In addition, the dishonesty risk contagion effect will reduce enterprises' refinancing ability. The local contagion effect of the dishonesty risk may cause the issuers' borrowing and financing amounts to decrease in the following year, and the global diffusion effect will lower the bond financing amount in the following year.
This paper makes three major contributions to the literature. (1) In terms of research perspective, it reveals the impact of dishonesty contagion on bond credit spreads and supplements the research on credit bond pricing. (2) In terms of research objects, previous research on network risk contagion mainly focused on local contagion effects rather than global diffusion effects. This paper distinguishes three types of contagion effects (direct contagion, local contagion, and global diffusion) and comprehensively reveals the transmission mechanism from the local to the whole, supplementing the research on network risk diffusion. (3) In terms of research methods, most research on network risk contagion examines the correlation of credit risk and network structure to test the network contagion characteristics of risk, ignoring the network heterogeneity of risk diffusion. When constructing risk contagion variables, this paper takes into account heterogeneous factors such as network structure characteristics, the location of both enterprises, and the risk sources in the network, supplementing the research on network risk.
|
|
Received: 22 December 2020
Published: 05 August 2022
|
|
|
|
|
| [1] |
曹廷求和刘海明,2016,《信用担保网络的负面效应:传导机制与制度诱因》,《金融研究》第1期,第145～159页。
|
| [2] |
陈道富和刘新海,2015,《我国担保圈大数据分析的初步发现》,《发展研究》第5期,第24～29页。
|
| [3] |
戴静静,2014,《政府干预下失衡的金融生态环境——以山东海龙担保圈为例》,《中国管理信息化》第12期,第71～73页。
|
| [4] |
刘海明、 王哲伟和曹廷求,2016,《担保网络传染效应的实证研究》,《管理世界》第4期,第81～96+188页。
|
| [5] |
吕静、王营和郭沛,2021,《担保网络风险传染机制:路径分析与实证检验》,《管理评论》第3期,第1～13页。
|
| [6] |
纳鹏杰、雨田木子和纳超洪,2017,《企业集团风险传染效应研究》,《会计研究》第3期,第53～60页。
|
| [7] |
施丹和姜国华,2013,《会计信息在公司债信用等级迁移中的预测作用研究》,《会计研究》第3期,第43～50+95页。
|
| [8] |
万良勇和魏明海,2009,《金融生态、利益输送与信贷资源配置效率——基于河北担保圈的案例研究》,《管理世界》第5期,第6～16+46+187页。
|
| [9] |
王雷,2019,《信用债市场在货币政策传导机制中的作用研究——基于信用利差的视角》,中国科学院大学博士学位论文。
|
| [10] |
王永钦、米晋宏、袁志刚和周群力,2014,《担保网络如何影响信贷市场——来自中国的证据》,《金融研究》第10期,第116～132页。
|
| [11] |
邢伟刚,2019,《担保链圈融资模式风险》,《区域治理》第35期,第179～181页。
|
| [12] |
杨国超和盘宇章,2019,《信任被定价了吗？——来自债券市场的证据》,《金融研究》第1期,第35～53页。
|
| [13] |
于立、于左和丁宁,2002,《信用、信息与规制——守信/失信的经济学分析》,《中国工业经济》第6期,第65～70页。
|
| [14] |
张春强、鲍群和盛明泉,2019,《公司债券违约的信用风险传染效应研究——来自同行业公司发债定价的经验证据》,《经济管理》第1期,第174～190页。
|
| [15] |
张维迎,2002,《法律制度的信誉基础》,《经济研究》第1期,第3～13+92～93页。
|
| [16] |
张泽旭、李鹏翔和郭菊娥,2012,《担保链危机的传染机制》,《系统工程》第4期,第25～31页。
|
| [17] |
Acemoglu, D., A. Ozdaglar and A. Tahbaz-Salehi, 2015, “Systemic Risk and Stability in Financial Networks,” American Economic Review, 105(2): 564～608.
|
| [18] |
Bai J., P. Collin-Dufresne, R. S. Goldstein and J. Helwege, 2015, “On Bounding Credit-Event Risk Premia,” The Review of Financial Studies, 28(9): 2608～2642.
|
| [19] |
Black, F. and J. C. Cox, 1976, “Valuing Corporate Securities: Some Effects of Bond Indenture Provisions,” The Journal of Finance, 31(2): 351～367.
|
| [20] |
Das, S. R., D. Duffie, N. Kapadia and L. Saita, 2007, “Common Failings: How Corporate Defaults are Correlated,” The Journal of Finance, 62(1): 93～117.
|
| [21] |
Gu S, Kelly B. T., Xiu D., 2020, “Empirical Asset Pricing via Machine Learning,” The Review of Financial Studies, 33(5): 2223～2273.
|
| [22] |
Jarrow, R. A. and S. M. Turnbull, 1995, “Pricing Derivatives on Financial Securities Subject to Credit Risk,” The Journal of Finance, 50(1): 53～85.
|
| [23] |
Jarrow, R. A., D. Lando and S. M. Turnbull, 1997, “A Markov Model for the Term Structure of Credit Risk Spreads,” The Review of Financial Studies, 10(2): 481～523.
|
| [24] |
Jorion P. and G. Zhang, 2009, “Credit Contagion from Counterparty Risk,” The Journal of Finance, 64(5): 2053～2087.
|
| [25] |
Leng, A., G. Xing and W. Fan, 2017, “Credit Risk Transfer in SME Loan Guarantee Networks,” Journal of Systems Science & Complexity, 30(05): 1084～1096.
|
| [26] |
Li, W., S. Ben, U. Ho mmel, S. Paterlini and J. Yu, 2019, “Default Contagion and Systemic Risk in Loan Guarantee Networks,” Accounting & Finance, 59: 1923～1946.
|
| [27] |
Merton, R. C., 1974, “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,” The Journal of Finance, 29(2): 449～470.
|
| [28] |
Page, L., et al., 1999, “The Pagerank Citation Ranking: Bringing Order to the Web”, Stan ford InfoLab, Technical Report.
|
|
|
|
